An intelligent service orchestration method and system for a cross-domain education heterogeneous system

Abstract

The application discloses an intelligent service arrangement method and system of a cross-domain education heterogeneous system, belongs to the technical field of distributed computing and task scheduling, and comprises the following steps: receiving a cross-domain education resource request data packet, analyzing and extracting business characteristic words and a global routing registration table identifier; based on the identifier, sending a state detection instruction to a plurality of heterogeneous nodes in a distributed network in parallel, acquiring node load data and a bottom layer data storage format identifier; based on the business characteristic words, the load data and the format identifier, executing a resource scheduling algorithm to construct a service arrangement execution path; based on the path, generating and distributing a data processing instruction, collecting and outputting education resource processing result data. The scheduling mechanism dynamically associates the business computing power demand with the node real-time load and the storage format difference, can effectively optimize the heterogeneous resource allocation, avoid high protocol conversion cost, and improve the service arrangement efficiency and system response speed in the cross-domain education heterogeneous environment.

Description

Technical Field

[0001] This invention relates to the field of distributed computing and task scheduling technology, and in particular to an intelligent service orchestration method and system for cross-domain heterogeneous education systems. Background Technology

[0002] Currently, with the deepening development of educational informatization, educational resources are often scattered across distributed networks containing multiple heterogeneous data storage nodes. When a system receives a request to access cross-domain educational resources, it faces complex tasks such as computing power scheduling, multi-node collaborative computing, and heterogeneous data format conversion. How to perform efficient service orchestration in an environment with dynamically changing loads on underlying nodes and heterogeneous data storage formats is a challenging problem in the field of distributed computing and digital data processing.

[0003] Among related technologies, Chinese invention patent with announcement number CN120315913B discloses a cloud-native container storage interface plugin, a method for allocating storage resources based on priority and attaching storage resources, including: an abstraction layer component for shielding the differences in storage resources, a storage management component for calculating storage service performance indicators and maintaining an available list, and a storage client component for connecting to the storage service end; its main process is that the system operation and maintenance administrator pre-registers the storage resources to be integrated, calculates the priority indicators of the storage resources, and allocates storage resources according to the priority indicators.

[0004] However, the aforementioned solutions primarily focus on storage priority allocation and static resource allocation within the underlying cloud-native environment, lacking a deep understanding and dynamic response capability for specific business request characteristics at the upper layers. In cross-domain education heterogeneous scenarios, this solution fails to extract business characteristics from request data packet payloads to dynamically assess actual computing power requirements. Furthermore, when planning task execution paths, it does not comprehensively consider the time consumed by underlying data storage format conversion between heterogeneous nodes in conjunction with network communication latency. This mechanism can easily lead to tasks being blindly assigned to high-load nodes or incurring high format conversion time costs in cross-node data interaction, resulting in low overall system computing resource utilization and slow data retrieval response, failing to meet the low-latency, high-concurrency intelligent service orchestration requirements of cross-domain education platforms. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an intelligent service orchestration method and system for cross-domain heterogeneous education systems. It employs a dynamic resource scheduling algorithm that extracts business feature words, combines node load data with underlying data storage format identifiers to calculate data exchange latency costs. This algorithm can construct the optimal service orchestration execution path with the goal of reducing data exchange latency costs and complete asynchronous processing and splicing output.

[0006] The above objectives can be achieved through the following approach:

[0007] A method for intelligent service orchestration in a cross-domain heterogeneous education system includes: receiving cross-domain educational resource acquisition request data packets through a network communication interface; parsing the cross-domain educational resource acquisition request data packets to extract business feature words and global routing registry identifiers; generating state probe instructions based on the global routing registry identifiers and concurrently sending the state probe instructions to multiple heterogeneous educational data storage nodes in a distributed network to obtain node load data and underlying data storage format identifiers returned by the heterogeneous educational data storage nodes; executing a resource scheduling algorithm based on the business feature words, the node load data, and the underlying data storage format identifiers to construct a service orchestration execution path; generating data processing instructions based on the service orchestration execution path, distributing the data processing instructions to the heterogeneous educational data storage nodes, collecting and outputting educational resource processing result data fed back by the heterogeneous educational data storage nodes.

[0008] Optionally, the step of parsing the cross-domain educational resource acquisition request data packet and extracting business feature words and global routing registry identifiers includes: performing a packet disassembly and extraction operation on the cross-domain educational resource acquisition request data packet according to a preset network transmission protocol to generate a data packet header feature field and a data packet payload content; performing a string hash matching extraction operation on the data packet payload content based on a preset business feature dictionary to generate business feature words; extracting the routing network identifier field from the data packet header feature field and using the routing network identifier field as the global routing registry identifier.

[0009] Optionally, the step of performing string hash matching extraction on the data packet payload content based on a preset business feature dictionary to generate business feature words includes: converting the data packet payload content into standard unified character encoding format text; performing a sliding window hash value calculation operation on the standard unified character encoding format text to generate a payload text hash sequence; comparing the payload text hash sequence with pre-stored hash nodes in the preset business feature dictionary, and outputting the corresponding words that match successfully as business feature words.

[0010] Optionally, the step of generating a state probe instruction based on the global routing registry identifier and concurrently sending the state probe instruction to multiple heterogeneous educational data storage nodes in the distributed network to obtain node load data and underlying data storage format identifier returned by the heterogeneous educational data storage nodes includes: inputting the global routing registry identifier to a preset global routing service center to perform a matching query operation to generate a set of node network layer addresses; extracting multiple network layer protocol addresses from the set of node network layer addresses, and establishing underlying socket communication connections with multiple heterogeneous educational data storage nodes based on the multiple network layer protocol addresses respectively; generating instruction data containing heartbeat detection messages, and using the instruction data as a state probe instruction; concurrently sending the state probe instruction through the underlying socket communication connection; receiving response messages from multiple heterogeneous educational data storage nodes through the underlying socket communication connection, extracting processor utilization and memory consumption values ​​from the response messages, and combining the processor utilization and memory consumption values ​​into the node load data; and extracting the underlying data storage format identifier from the data dictionary description field of the response messages.

[0011] Optionally, the method further includes: querying a matching protocol conversion mapping table in a preset local protocol conversion configuration library based on the underlying data storage format identifier; and executing a format conversion matching algorithm based on the protocol conversion mapping table to generate data format reading rules.

[0012] Optionally, the step of executing a resource scheduling algorithm and constructing a service orchestration execution path based on the business feature words, the node load data, and the underlying data storage format identifier includes: executing a computing power demand assessment algorithm based on the business feature words to generate a target computing resource consumption threshold; comparing the node load data of multiple heterogeneous educational data storage nodes with the target computing resource consumption threshold to select nodes that meet the computing power requirements as candidate educational data storage nodes; extracting the underlying data storage format identifier of the candidate educational data storage nodes and calculating the data exchange latency cost between the candidate educational data storage nodes; and generating the service orchestration execution path containing instructions on node interaction order and resource mapping relationship based on the principle of reducing the data exchange latency cost.

[0013] Optionally, extracting the underlying data storage format identifier of the candidate educational data storage nodes and calculating the data exchange delay cost value between the candidate educational data storage nodes includes: comparing the underlying data storage format identifiers of any two candidate educational data storage nodes to generate a format conversion requirement status value; when the format conversion requirement status value meets a preset trigger condition, extracting the corresponding format conversion delay time value based on a pre-stored format conversion time consumption matrix; obtaining the network communication delay time value between the candidate educational data storage nodes, and performing a weighted summation of the network communication delay time value and the format conversion delay time value to generate a data exchange delay cost value.

[0014] Optionally, the step of generating data processing instructions based on the service orchestration execution path, distributing the data processing instructions to the heterogeneous educational data storage nodes, collecting educational resource processing result data fed back by the heterogeneous educational data storage nodes, and outputting the educational resource processing result data includes: extracting resource mapping relationship instructions within the service orchestration execution path, generating data processing instructions in conjunction with a preset database connection pool protocol; sending the data processing instructions to the heterogeneous educational data storage nodes according to a preset execution sequence; opening a local asynchronous communication receiving port, receiving the educational resource processing result data stream returned by the heterogeneous educational data storage nodes through the local asynchronous communication receiving port; performing a splicing and integration operation on the educational resource processing result data stream to generate educational resource processing result data, and outputting the educational resource processing result data through a user interface.

[0015] Optionally, the step of extracting resource mapping relationship instructions within the service orchestration execution path and generating data processing instructions in conjunction with a preset database connection pool protocol includes: performing a syntax conversion operation on the resource mapping relationship instructions to generate a standard query text stream corresponding to the underlying data storage format identifier; adding an identity authentication identifier and a data packet checksum to the standard query text stream based on the preset database connection pool protocol, and encapsulating it to generate data processing instructions.

[0016] Based on the same inventive concept, this invention also provides an intelligent service orchestration system for cross-domain heterogeneous education systems, the system comprising:

[0017] The request data packet parsing module is used to receive cross-domain educational resource acquisition request data packets through a network communication interface, perform parsing operations on the cross-domain educational resource acquisition request data packets, and extract business feature words and global routing registry identifiers.

[0018] The node status detection module is used to generate status detection instructions based on the global routing registry identifier, and concurrently send the status detection instructions to multiple heterogeneous educational data storage nodes in the distributed network to obtain the node load data and underlying data storage format identifier returned by the heterogeneous educational data storage nodes.

[0019] The orchestration execution path construction module is used to construct service orchestration execution paths based on the business feature words, the node load data, and the underlying data storage format identifier by executing a resource scheduling algorithm.

[0020] The instruction distribution and result output module is used to generate data processing instructions based on the service orchestration execution path, distribute the data processing instructions to the heterogeneous educational data storage nodes, collect the educational resource processing result data fed back by the heterogeneous educational data storage nodes, and output the educational resource processing result data.

[0021] Compared with the prior art, the present invention has the following advantages:

[0022] 1. By deeply analyzing cross-domain educational resource retrieval request data packets to extract business feature words, and concurrently sending status probe commands to multiple heterogeneous educational data storage nodes in the distributed network, the current node load data and underlying data storage format identifiers are obtained. By comparing the node load data with computing resource consumption thresholds generated based on business features, candidate nodes meeting the computing power requirements are accurately selected. This probe mechanism dynamically matches business needs with the real-time computing power of the underlying heterogeneous nodes, avoiding node overload caused by task allocation imbalances and improving the rationality and execution efficiency of resource scheduling in the distributed heterogeneous system.

[0023] 2. In constructing the service orchestration execution path, the overall time consumption caused by multi-node collaboration was fully considered. The underlying data storage format identifiers of candidate educational data storage nodes were extracted to calculate the data exchange latency cost between nodes. The format conversion latency caused by differences in the underlying data storage format was weighted and summed with the network communication latency. Based on the principle of reducing this latency cost, a service orchestration execution path including the node interaction order was generated. This design enables task scheduling to proactively avoid the high overhead of cross-platform data conversion, minimize the time loss of cross-domain data interaction, and ensure that resource requests are processed with minimal latency.

[0024] 3. It possesses heterogeneous environment adaptability and high-concurrency data throughput capabilities. Based on the acquired underlying data storage format identifier, it can generate data format reading rules within a preset local protocol conversion configuration library. During the task execution phase, it generates and distributes data processing instructions in conjunction with a preset database connection pool protocol, while simultaneously opening a local asynchronous communication receiving port to receive the returned educational resource processing result data stream. Finally, it performs a splicing and integration operation on the data for output. This asynchronous receiving and protocol conversion mechanism effectively shields the heterogeneity of the underlying storage, ensuring the stable aggregation and efficient output of educational resource data in complex networks.

[0025] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart illustrating an intelligent service orchestration method for a cross-domain heterogeneous education system according to an embodiment of the present invention.

[0028] Figure 2 This is a schematic diagram of the candidate node computing power evaluation model for an intelligent service orchestration method for a cross-domain heterogeneous education system according to an embodiment of the present invention.

[0029] Figure 3 This is a schematic diagram of a comparison model of data exchange delay costs between nodes in an intelligent service orchestration method for a cross-domain heterogeneous education system according to an embodiment of the present invention.

[0030] Figure 4 This is a schematic diagram of the structure of an intelligent service orchestration system for a cross-domain heterogeneous education system according to an embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Reference Figure 1 One embodiment of the present invention proposes an intelligent service orchestration method for cross-domain heterogeneous education systems. It adopts a dynamic resource scheduling algorithm that extracts business feature words, combines node load data and underlying data storage format identifiers to calculate data exchange latency costs. This method can construct the optimal service orchestration execution path with the goal of reducing data exchange latency costs and complete asynchronous processing and splicing output.

[0033] The method described in this embodiment specifically includes:

[0034] The system receives cross-domain educational resource acquisition request data packets through a network communication interface, performs parsing operations on the cross-domain educational resource acquisition request data packets, and extracts business feature words and global routing registry identifiers.

[0035] Based on the global routing registry identifier, a state probe instruction is generated and concurrently sent to multiple heterogeneous educational data storage nodes in the distributed network to obtain the node load data and underlying data storage format identifier returned by the heterogeneous educational data storage nodes.

[0036] Based on the business feature words, the node load data, and the underlying data storage format identifier, a resource scheduling algorithm is executed to construct a service orchestration execution path;

[0037] Data processing instructions are generated based on the service orchestration execution path, and the data processing instructions are distributed to the heterogeneous educational data storage nodes. The educational resource processing result data fed back by the heterogeneous educational data storage nodes is collected and output.

[0038] Specifically, the system receives cross-domain educational resource acquisition request data packets through a network communication interface and performs deep parsing to extract business feature words and global routing registry identifiers. Based on the global routing registry identifiers, state probe commands are generated and concurrently sent to multiple heterogeneous educational data storage nodes within the distributed network to dynamically obtain real-time node load data and underlying data storage format identifiers returned by each node. Building upon this, business feature words, node load data, and underlying data storage format identifiers are integrated as multi-dimensional decision parameters to execute a resource scheduling algorithm to construct the optimal service orchestration execution path. According to this execution path, corresponding data processing commands are generated and distributed to designated heterogeneous educational data storage nodes. The system also centrally collects the educational resource processing results data returned by each node to complete the output. Through the mechanism of concurrently sending state probe commands, the system can accurately and in real-time grasp the current computational load pressure and basic data structure attributes of each heterogeneous node in the underlying distributed network, overcoming the drawback of the lack of awareness of the underlying resource status under traditional blind scheduling mechanisms. During the service orchestration and execution path construction phase, the macro-level business characteristics of the requesting end are deeply integrated with the real-time load and storage format identifiers of the micro-nodes. This enables intelligent scheduling of computing tasks to the nodes with the best computing power and the lowest format conversion cost. This not only effectively avoids single-point network overload but also reduces data conversion time losses during cross-heterogeneous platform interactions, optimizes the overall utilization of system computing resources, and improves the response speed and processing efficiency of cross-domain education platforms when facing high-concurrency resource requests.

[0039] Optionally, the step of parsing the cross-domain educational resource acquisition request data packet and extracting business feature words and global routing registry identifiers includes:

[0040] According to a preset network transmission protocol, the data packet for obtaining cross-domain educational resources is unpacked and extracted to generate the header feature field of the data packet and the payload content of the data packet.

[0041] Based on a preset business feature dictionary, a string hash matching extraction operation is performed on the data packet payload content to generate business feature words;

[0042] Extract the routing network identifier field from the feature fields of the data packet header, and use the routing network identifier field as the global routing registry identifier.

[0043] Optionally, the step of performing string hash matching extraction on the data packet payload content based on a preset business feature trie to generate business feature words includes:

[0044] Convert the data packet payload content into a standard unified character encoding format text;

[0045] Perform a sliding window hash value calculation operation on the standard unified character encoding format text to generate a payload text hash sequence;

[0046] The payload text hash sequence is compared with the pre-stored hash nodes in the preset business feature dictionary tree, and the corresponding words that match successfully are output as business feature words.

[0047] Specifically, according to the preset network transmission protocol, the data packets for cross-domain educational resource acquisition requests are disassembled and extracted. This disassembly and extraction operation is a technical action performed by the network communication receiver to parse and strip the header and trailer control information of the data frame layer by layer from bottom to top according to the protocol stack to restore the original application layer payload, generating data packet header feature fields and data packet payload content; extracting the routing network identifier field from the data packet header feature fields, and using the routing network identifier field as the global routing registry identifier; converting the data packet payload content into standard unified character encoding format text, which is a text data stream that uses internationally accepted standards for character and number mapping to ensure consistent parsing expression across heterogeneous nodes; and performing a sliding window hash value calculation operation on the standard unified character encoding format text. This operation maintains a fixed-length character reading interval and uses the difference between characters in adjacent intervals to quickly and incrementally update the interval hash feature value as it moves across the text stream. A multinomial rolling hash algorithm is used to calculate the hash value of a fixed-length character window. As the window slides character by character across the text, the hash value of the current window is quickly recursively derived from the hash value of the previous window, generating a payload text hash sequence. The formula for the sliding window hash value recursion is as follows:

[0048] ;

[0049] in the formula The hash value representing the current sliding window. This represents the hash value of the previous sliding window. The Unicode value representing the character that was moved out of the window. The variable is a uniformly encoded value representing the character moving into the window. 'a' represents a prime number base constant set based on the principle of balanced hash distribution. 'L' represents a constant length of characters contained in the sliding window. 'm' represents a preset large prime number modulo constant to prevent memory hash collisions. The payload text hash sequence is compared with pre-stored hash nodes in the preset business feature dictionary. The preset business feature dictionary is a memory index structure that uses a multi-branch tree data structure to map and store professional terms in the education field according to character hash values ​​to achieve ultra-fast prefix matching. The pre-stored hash nodes are memory data units in the multi-branch tree data structure used to store hash mapping values ​​of professional terms for equality verification. The string hash matching extraction action is the process of mapping the text string to a fixed-length feature value and performing equality verification with the pre-stored value index set to quickly locate the target word. When the hash node on the traversal path of the dictionary is completely consistent with the payload text hash sequence, the corresponding word with a successful match is output as the business feature word.

[0050] For example, a cross-domain educational resource retrieval request data packet is received. A packet unpacking and extraction operation is performed to generate a data packet header feature field containing the source Internet Layer address 10.0.1.55 and a data packet payload containing the English string "CHEMX". The source Internet Layer address 10.0.1.55 in the data packet header feature field is extracted as a global routing registry identifier. The underlying transcoding library is called to convert the data packet payload content into standard unified character encoding format text. A standard character lookup table is consulted to obtain the decimal unified encoding values ​​corresponding to the characters C, H, E, M, and X, which are 67, 72, 69, 77, and 88 respectively. Based on the principle of balanced hash distribution algorithm, the prime number base constant a is configured to be 10. The character length constant L contained in the sliding window is set to 4 according to the prefix length of common educational subjects. To prevent memory hash collisions, the large prime number modulo constant m is set to 100003. The first four characters of the text, CHEM, are subjected to polynomial expansion to calculate the initial hash value. The calculation process is as follows: That is, 74967. Taking the modulo of the large prime number 100003, the initial hash value of 74967 is retained. Then, the character range is controlled to slide one character step to the right on the text, thus moving out the first character C and into the new character X at the end. The hash value of the current sliding window is calculated using a polynomial recursive formula. Specifically... That is, 79758. After taking the modulo of the large prime number 100003, the current sliding window hash value 79758 is generated. The results of the two calculations are combined to generate a payload text hash sequence containing 74967 and 79758. Then, the preset business feature trie is accessed through the memory bus. The values ​​in the payload text hash sequence are compared with the pre-stored hash nodes in the multi-branch tree. The pre-stored hash node 74967 with a completely equal value is successfully located on the chemistry subject branch of the trie. Finally, the educational vocabulary output of chemistry mapped to this node is extracted and used as the business feature word.

[0051] Optionally, the step of generating state probe commands based on the global routing registry identifier, and concurrently sending the state probe commands to multiple heterogeneous educational data storage nodes in the distributed network to obtain the node load data and underlying data storage format identifier returned by the heterogeneous educational data storage nodes includes:

[0052] The global route registry identifier is input into the preset global route service center to perform a matching query operation, generating a set of node network layer addresses;

[0053] Extract multiple network layer protocol addresses from the node network layer address set, and establish underlying socket communication connections with multiple heterogeneous educational data storage nodes based on the multiple network layer protocol addresses respectively;

[0054] Generate instruction data containing a heartbeat detection message, and use the instruction data as a status detection instruction;

[0055] The status detection command is sent concurrently through the underlying socket communication connection;

[0056] Receive response messages from multiple heterogeneous educational data storage nodes through the underlying socket communication connection, extract processor utilization and memory consumption values ​​from the response messages, and combine the processor utilization and memory consumption values ​​into the node load data.

[0057] Extract the underlying data storage format identifier from the data dictionary description field of the response message.

[0058] Specifically, the global routing registry identifier is input into the preset global routing service center to perform a matching query operation. The preset global routing service center is a directory service cluster responsible for mapping logical business identifiers to physical network layer addresses in a cross-domain distributed network to achieve cross-domain addressing, generating a set of node network layer addresses; extracting multiple network layer protocol addresses from the set of node network layer addresses, and establishing underlying socket communication connections with multiple heterogeneous educational data storage nodes based on these multiple network layer protocol addresses. The underlying socket communication connections are underlying network channels built on top of transport layer protocols to provide end-to-end full-duplex reliable byte stream transmission; and generating instructions containing heartbeat detection messages. The data, including heartbeat detection messages, is a lightweight diagnostic data packet used to detect the liveness status of target nodes and network interaction latency, without containing specific business payloads. Command data is used as status detection commands. Status detection commands are sent concurrently through the underlying socket communication connection. Response messages from multiple heterogeneous educational data storage nodes are received through the underlying socket communication connection. Processor utilization and memory consumption values ​​are extracted from the response messages and combined to form node load data. Node load data is a core evaluation indicator that quantitatively reflects the current hardware resource consumption level of the server for subsequent scheduling decisions. The combined calculation formula is as follows:

[0059] ;

[0060] in the formula Represents node load data. This represents the percentage of processor utilization extracted from the node. This represents the percentage of memory consumed when extracting data from a node. This represents the processor weight coefficient configured based on the characteristics of computationally intensive business operations. This represents the memory weight coefficient configured based on memory read / write characteristics; the underlying data storage format identifier is extracted from the data dictionary description field of the response message. The data dictionary description field is the metadata declaration area in the message header used to standardize the payload data structure definition and persistence engine type.

[0061] For example, the global routing registry identifier 192.168.1.1 is input into the preset global routing service center to perform a matching query operation, retrieve the bound physical nodes, generate a set of node network layer addresses containing 10.0.0.11 and 10.0.0.12, extract these two network layer protocol addresses, call the system network component to establish low-level socket communication connections with the transmission control protocol ports of the two heterogeneous educational data storage nodes, and generate instruction data containing hexadecimal heartbeat detection message 0xAA55 as a status probe instruction. Status probe commands are sent concurrently to 10.0.0.11 and 10.0.0.12 via the underlying socket communication connection. Response messages from both nodes are then received. The processor utilization (75) and memory consumption (60) values ​​are extracted from the response message from the first node (10.0.0.11), and the processor utilization (40) and memory consumption (80) values ​​are extracted from the response message from the second node (10.0.0.12). Based on the characteristic that cross-domain acquisition of educational resources is a computationally intensive parsing task, processor weight coefficients are configured. The value is 0.6, which sets the memory weight coefficient corresponding to the memory read / write characteristics. The value is 0.4. Substituting the hardware consumption data of the first node into the combined calculation formula, the result is... The result is 69. The node load data for the first node is 69. Substituting the hardware consumption data of the second node into the combined calculation formula, the result is... The result is 56, and the node load data of the second node is 56. At the same time, the data dictionary description field of the response message is read. The underlying data storage format identifier is extracted from the metadata declaration area of ​​the first node 10.0.0.11 as JSON format, and from the metadata declaration area of ​​the second node 10.0.0.12 as XML format. Thus, the extraction of the status evaluation parameters of each heterogeneous node is completed.

[0062] Optionally, the method further includes:

[0063] Based on the underlying data storage format identifier, a matching protocol conversion mapping table is queried in the preset local protocol conversion configuration library;

[0064] Based on the protocol conversion mapping table, a format conversion matching algorithm is executed to generate data format reading rules.

[0065] Specifically, based on the underlying data storage format identifier, a matching protocol conversion mapping table is queried in the preset local protocol conversion configuration library. The preset local protocol conversion configuration library is a set of static configuration files pre-loaded locally by the system to store the connection driver configuration and data model parsing specifications of various heterogeneous data sources. The protocol conversion mapping table is a structured key-value dictionary that precisely defines the mapping relationship between the basic data types, network communication transmission protocols, and query statement syntax between different storage engines in a cross-domain environment. Based on the protocol conversion mapping table, a format conversion matching algorithm is executed. The format conversion matching algorithm is a compilation and conversion logic that deconstructs the unified standard request instructions within the system and rearranges and reorganizes them according to the rules of the mapping table into a native executable code sequence of the target underlying storage system by constructing an abstract syntax tree. This generates data format reading rules, which are comprehensive operational constraint strategies that package and encapsulate the target database's underlying communication port, dedicated driver execution path, underlying native query instruction fragments, and response data stream deserialization scheme.

[0066] For example, the underlying data storage format identifier of the target heterogeneous educational data storage node is obtained as BSON document storage format. The system directly accesses the preset local protocol conversion configuration library, using the BSON document storage format as the search key for matching and locating the target. It then queries the corresponding protocol conversion mapping table, which clearly records the uniformly defined conversion constraints for extracting attributes from the document object model using the structured query language. This table specifies the target set mapping method and binds it to the default underlying communication port 27017. Subsequently, based on this protocol conversion mapping table, the format conversion matching algorithm is initiated. The internal standard request statement, which uniformly requests the acquisition of mathematics educational resources with a resource number equal to 100, is parsed and broken down into action types of query and... The target resource is an abstract syntax tree node with a mathematical parameter and a resource number equal to 100. Based on the syntax correspondence rules in the mapping table, the abstract syntax tree node is recompiled and compiled into a query code snippet that can be directly recognized and executed by the document database. Specifically, it is represented by db.math.find({"id":100}). The native query code snippet, the specified communication port number 27017, the corresponding dedicated document parsing driver component call path, and the operation function reference for deserializing the node return result into standard structured data are uniformly packaged and encapsulated to generate data format reading rules specific to this BSON document data storage node, thus establishing the underlying technical execution path for subsequent accurate cross-domain data retrieval.

[0067] Optionally, the step of executing a resource scheduling algorithm and constructing a service orchestration execution path based on the business feature words, the node load data, and the underlying data storage format identifier includes:

[0068] Based on the business feature words, a computing power demand assessment algorithm is executed to generate a target computing resource consumption threshold.

[0069] The node load data of multiple heterogeneous educational data storage nodes are compared with the target computing resource consumption threshold to select nodes that meet the computing power requirements as candidate educational data storage nodes.

[0070] Extract the underlying data storage format identifier of the candidate educational data storage node, and calculate the data exchange latency cost between the candidate educational data storage nodes;

[0071] Based on the principle of reducing the cost of data exchange latency, the service orchestration execution path is generated, which includes instructions on node interaction order and resource mapping relationship.

[0072] Specifically, a computing power demand assessment algorithm is executed based on business feature words. This algorithm configures a basic consumption benchmark based on the complexity of the subject area corresponding to the business request and linearly amplifies it by combining real-time concurrency and heat intensity to estimate the lower limit of the required computing resources. It then generates a target computing resource consumption threshold, which is the minimum quantified standard of available computing power on a node necessary to ensure that this type of business request can be processed smoothly within a specified latency period. The calculation formula is as follows:

[0073] ;

[0074] in the formula This represents the threshold for the target computing resource consumption. This represents the basic computing power consumption value corresponding to the business feature words in the pre-established mapping dictionary. This represents the current global concurrency coefficient. After obtaining the target computing resource consumption threshold, the node load data of multiple heterogeneous educational data storage nodes are compared with the target computing resource consumption threshold. The comparison operation is a logical judgment process that compares the current actual remaining computing power of a node with the calculated minimum threshold to determine whether the task admission conditions are met. Nodes that meet the computing power requirements are selected as candidate educational data storage nodes. Candidate educational data storage nodes are underlying physical storage devices with remaining available computing power greater than or equal to the target computing resource consumption threshold. The underlying data storage format identifier of the candidate educational data storage nodes is extracted, and the data exchange latency cost between the candidate educational data storage nodes is calculated. The data exchange latency cost is a composite penalty assessment that comprehensively measures the transmission time of data in the network physical link and the parsing and reconstruction time caused by inconsistent storage structures. The calculation formula is as follows:

[0075] ;

[0076] in the formula This represents the numerical value of data exchange latency. This represents the network transmission time weighting parameter set based on network bandwidth status. This represents the basic network communication latency between two candidate educational data storage nodes. This represents the format conversion time weighting parameter set based on the degree of data structure differences. When the underlying data storage format identifiers are inconsistent, the system consults the built-in conversion time matrix to obtain the format conversion latency value. Finally, based on the principle of reducing data exchange latency costs, a service orchestration execution path is generated, containing instructions on node interaction order and resource mapping relationships. The node interaction order is a data flow sequence between nodes planned according to the graph theory shortest path algorithm, with the optimization objective of minimizing global latency costs. The resource mapping relationship instructions are control statements that clarify the underlying tablespace query boundaries and data merging strategies that each available node is responsible for in the distributed collaborative computing task. The service orchestration execution path is a packaged encapsulation of the aforementioned flow sequence and specific execution instructions. Figure 2 The diagram shows a candidate node computing power evaluation model.

[0077] For example, the business feature term is obtained as "chemistry". The computational power demand assessment algorithm is executed, and the system's built-in subject complexity mapping dictionary is consulted to obtain the basic computational power consumption value corresponding to "chemistry", which is 150. The network status monitoring module reads the global concurrency coefficient corresponding to the current peak network request concurrency as 1.2. Substituting this into the target computational resource consumption threshold formula, 150 × 1.2 = 180 is calculated, generating a target computational resource consumption threshold of 180. The node load data of four heterogeneous educational data storage nodes in the distributed network is read. Here, node load data specifically refers to the real-time available idle computational power assessment value of the nodes. The available idle computational power assessment value of node A is 220, node B is 160, node C is 250, and node D is 210. The node load data was compared with the target computing resource consumption threshold of 180. It was found that node A had a load of 220 > 180, node C had a load of 250 > 180, and node D had a load of 210 > 180. Node B had a load of 160 < 180 (failing the hardware access check). Based on this, nodes A, C, and D were selected as candidate educational data storage nodes that met the computing power requirements. The underlying data storage format identifiers of the three candidate educational data storage nodes were extracted. Nodes A and D were in JSON document format, while node C was in relational table format. The data exchange latency cost between each pair of candidate educational data storage nodes was calculated. Based on the current network communication status and format conversion overhead, the network transmission time weight parameter was set to 0.6, and the format conversion time weight parameter was set to 0.4. To evaluate the cost between nodes A and C, due to the different formats, the conversion latency matrix shows a value of 40. The network probe measures a base network communication latency of 20. Substituting these values ​​into the calculation formula, the data exchange latency cost between nodes A and C is calculated to be 0.6 × 20 + 0.4 × 40 = 12 + 16 = 28. To evaluate the cost between nodes A and D, since both nodes are in JSON document format and no reconstruction is needed, the conversion latency is 0. The base network communication latency is measured to be 30. Substituting these values ​​into the calculation formula, the data exchange latency cost between nodes A and D is calculated to be 0.6 × 30 + 0.4 × 0 = 18 + 0 = 18. Based on the principle of reducing the cost of data exchange latency, a comparison revealed that the latency cost of interaction between node A and node D is lower than that of interaction between node A and node C (i.e., 18 < 28). Therefore, the node interaction order that prioritizes the aggregation of educational resource data between node A and node D was established. Based on the storage architecture of both nodes, resource mapping instructions were generated, requiring node A to extract chemical theory knowledge structure blocks and node D to extract corresponding chemical experiment video resource blocks. Finally, the node interaction order and resource mapping instructions were combined into a service orchestration execution path that can be directly dispatched to each computing sub-node.

[0078] Optionally, extracting the underlying data storage format identifier of the candidate educational data storage node and calculating the data exchange latency cost between the candidate educational data storage nodes includes:

[0079] Compare the underlying data storage format identifiers of any two candidate educational data storage nodes to generate a format conversion requirement status value;

[0080] When the format conversion requirement status value meets the preset triggering condition, the corresponding format conversion delay time value is extracted based on the pre-stored format conversion time consumption matrix.

[0081] Obtain the network communication delay time value between the candidate educational data storage nodes, and perform a weighted summation of the network communication delay time value and the format conversion delay time value to generate a data exchange delay cost value.

[0082] Specifically, by comparing the underlying data storage format identifiers of any two candidate educational data storage nodes, a format conversion requirement status value is generated. This status value is a logical identifier parameter that quantifies whether the underlying storage structures of the two interacting nodes are in a heterogeneous state, thus requiring cross-platform data model mapping and syntax translation. When the format conversion requirement status value meets a preset trigger condition (a logical threshold activated when the format identifier strings of the two nodes are inconsistent), the corresponding format conversion delay time value is extracted based on a pre-stored format conversion time consumption matrix. This matrix is ​​a two-dimensional mapping table structure pre-calibrated and loaded by the system, recording the baseline time required for mutual conversion between various known heterogeneous data structures. The network communication delay time value between the candidate educational data storage nodes is obtained, and the network communication delay time value and the format conversion delay time value are weighted and summed to generate a data exchange delay cost value. The weighted summation formula is as follows:

[0083] ;

[0084] in the formula This represents the numerical value of data exchange latency. This represents the obtained network communication latency value. This represents a network communication weight parameter configured based on the current level of physical network congestion and bandwidth margin. This represents the format conversion delay time value extracted from the matrix. This represents the format conversion weight parameters based on the encoding / decoding performance configuration of the node's central processing unit. For example... Figure 3 The diagram shows a comparison of the latency costs of data exchange between nodes.

[0085] For example, a pairing evaluation is performed on the first and second candidate educational data storage nodes selected in the task scheduling pool. The underlying data storage format identifier of the first candidate educational data storage node is extracted as a relational table format, and the underlying data storage format identifier of the second candidate educational data storage node is extracted as a graph data format. A comparison reveals a significant difference and mismatch between the underlying data storage format identifiers of the two. Based on this, a format conversion requirement state value of 1 is generated, representing the need for heterogeneous conversion. It is detected that the format conversion requirement state value 1 meets the preset trigger condition for heterogeneous processing. Then, the format conversion time consumption matrix residing in local memory is accessed. A precise coordinate lookup is performed using the relational table format as the row index and the graph data format as the column index. The corresponding format conversion latency value is extracted as 45. At the same time, the underlying link probe is called to calculate the network communication latency value between the first and second candidate educational data storage nodes, which is 20. Based on the actual hardware operation situation where the current cross-domain network frequency band bandwidth is sufficient and the node resolution computing power has load fluctuations, a network communication weight parameter with emphasis on computing power penalty is configured. The value is 0.4, which is the configuration format conversion weight parameter. The value is 0.6. Substituting the extracted basic data into the weighted summation formula, the summation is 35. Finally, the value of 35 is generated to evaluate the data exchange delay cost of the interaction between the two nodes.

[0086] Optionally, the step of generating data processing instructions based on the service orchestration execution path, distributing the data processing instructions to the heterogeneous educational data storage nodes, collecting educational resource processing result data fed back by the heterogeneous educational data storage nodes, and outputting the educational resource processing result data includes:

[0087] Extract the resource mapping relationship instructions within the service orchestration execution path, and generate data processing instructions by combining them with a preset database connection pool protocol;

[0088] The data processing instructions are sent to the heterogeneous educational data storage node according to a preset execution sequence;

[0089] Open a local asynchronous communication receiving port and receive the educational resource processing result data stream returned by the heterogeneous educational data storage node through the local asynchronous communication receiving port;

[0090] The data stream of educational resource processing results is spliced ​​and integrated to generate educational resource processing result data, which is then output through a user interface.

[0091] Specifically, the resource mapping instructions within the service orchestration execution path are extracted. These instructions undergo syntax transformation, which involves lexical analysis and abstract syntax tree reconstruction at the compiler front-end to accurately translate high-level business scheduling intentions into query code natively supported by the target underlying data storage engine. This generates a standard query text stream corresponding to the underlying data storage format identifier. The standard query text stream is a clean, executable data query code segment that conforms to the target database's proprietary syntax specifications and does not contain network control encapsulation information. Based on a pre-configured database connection pool protocol, which is a pre-configured underlying network framework specification for standardizing database communication link reuse, lifecycle management, and secure encapsulation of interactive requests, an authentication identifier and a data packet verification code are added to the standard query text stream. The authentication identifier is a token dynamically generated using cryptographic algorithms and system credentials to verify the access rights and legitimate identity of the request initiator. The data packet verification code is an anti-counterfeiting verification sequence generated by performing a specific statistical algorithm on the message payload to detect whether data bit flipping or malicious tampering has occurred during cross-domain physical transmission at the receiving end. The above data segments are then packaged and encapsulated to generate data processing instructions.

[0092] For example, the resource mapping instruction extracted from the service orchestration execution path is a request to extract document resource number five. The corresponding underlying data storage format is identified as a relational table format. A syntax conversion operation is performed on the resource mapping instruction, compiling and translating it into a standard query text stream natively supported by the relational database: SELECT FROM doc WHERE id=5. Then, combined with the security policy framework of the preset database connection pool protocol, an authentication identifier is generated for the standard query text stream. The pre-assigned basic static security credential value to the requesting end is read as 1024, and the currently concurrently allocated task sequence number is obtained as 50. The basic static security credential value and the task sequence number are multiplied to get 1024, multiplied by 50, and summed to obtain 51200. 51200 is confirmed as the authentication identifier for this request. Then, to ensure the integrity of data transmitted across domains... The data packet checksum is calculated by using a preset cumulative checksum algorithm to extract the core table name "doc" from the standard query text stream. The decimal values ​​of the characters 'd', 'o', and 'c' are obtained from a general standard character lookup table: 100, 111, and 99 respectively. These three core character codes are then added to the authentication identifier value, resulting in 100 + 111 + 99 + 51200 (310 + 51200), which sums to 51510. This value is used as the data packet checksum. Finally, the parsed and compiled standard query text stream, the authentication identifier (value 51200), and the data packet checksum (value 51510) are sequentially concatenated according to the header, payload, and trailer frame structure specified by network communication regulations. This concatenation generates the final data processing instruction that can be directly sent to heterogeneous data storage nodes via the physical network card.

[0093] Optionally, the step of extracting the resource mapping relationship instructions within the service orchestration execution path and generating data processing instructions in conjunction with a preset database connection pool protocol includes:

[0094] The resource mapping relationship instruction is subjected to syntax conversion to generate a standard query text stream corresponding to the underlying data storage format identifier;

[0095] Based on the preset database connection pool protocol, an identity authentication identifier and a data packet verification code are added to the standard query text stream, and data processing instructions are encapsulated and generated.

[0096] Specifically, the resource mapping instructions within the service orchestration execution path are extracted. These instructions undergo syntax transformation, a technical process that uses lexical analysis and abstract syntax tree reconstruction at the compiler front-end to precisely translate high-level business scheduling intentions into query code natively supported by the target underlying data storage engine. This generates a standard query text stream corresponding to the underlying data storage format identifier. The standard query text stream is a clean, executable data query code segment that conforms to the target database's proprietary syntax specifications and does not contain network control encapsulation information. Based on a pre-configured database connection pool protocol—a pre-configured underlying network framework specification for standardizing database communication link reuse, lifecycle management, and secure encapsulation of interactive requests—an authentication identifier and a data packet checksum are added to the standard query text stream. The authentication identifier is a token dynamically generated using cryptographic algorithms and system credentials to verify the access rights and legitimate identity of the request initiator. The data packet checksum is an anti-counterfeiting verification sequence generated by performing a specific statistical algorithm on the message payload to detect whether data bit flipping or malicious tampering has occurred during cross-domain physical transmission at the receiving end. The calculation formula for the data packet checksum is as follows:

[0097] ;

[0098] in the formula This represents the generated data packet checksum. This represents the pre-assigned identity authentication identifier value. This represents the preset security weight factor configured according to network security levels, where N represents the total length of characters contained in the standard query text stream. This represents the decimal Unicode value corresponding to the j-th character in the standard query text stream. This represents a preset modulo-major prime number used to limit the length of the checksum; it packages the standard query text stream, identity authentication identifier, and calculated data packet checksum into a unified package and encapsulates it to generate data processing instructions.

[0099] For example, the resource mapping instruction for the relational education data storage node extracted from the service orchestration execution path is to read the chemistry subject record. This instruction undergoes syntax conversion to reconstruct a standard query text stream, specifically the string READCHEM, corresponding to the underlying relational data storage format. Based on a preset database connection pool protocol, this instruction is securely encapsulated, and the currently assigned identity authentication identifier value is extracted from the system security credential center. The value is 1024, which is the preset security weight factor for the current internal network communication channel configuration. The value is 3, which is a preset large prime number for modulo operation to control the message header length. The result is 997. Next, anti-counterfeiting sequence calculation is performed on the standard query text stream READCHEM. Querying the standard character mapping table, the decimal codes for these nine characters, including spaces, are: R = 82, E = 69, A = 65, D = 68, space = 32, C = 67, H = 72, E = 69, and M = 77. Summing these nine character codes yields a sum of 601. Substituting this into the data packet verification code calculation formula, the product of the authentication identifier and the security weight is first calculated. Adding the product to the sum of the characters, we get 3072 + 601 = 3673. Finally, we perform a modulo operation on the preset large prime number 997. The generated data packet verification code value is 682. Finally, the clean query code fragment READCHEM, the identity authentication identifier with a token value of 1024, and the data packet verification code with a value of 682 are concatenated according to the preset network frame format and encapsulated to generate a complete data processing instruction with anti-tampering verification capabilities.

[0100] Reference Figure 4 Based on the same inventive concept, this invention also provides an intelligent service orchestration system for cross-domain heterogeneous education systems, the system comprising:

[0101] The request data packet parsing module is used to receive cross-domain educational resource acquisition request data packets through a network communication interface, perform parsing operations on the cross-domain educational resource acquisition request data packets, and extract business feature words and global routing registry identifiers.

[0102] The node status detection module is used to generate status detection instructions based on the global routing registry identifier, and concurrently send the status detection instructions to multiple heterogeneous educational data storage nodes in the distributed network to obtain the node load data and underlying data storage format identifier returned by the heterogeneous educational data storage nodes.

[0103] The orchestration execution path construction module is used to construct service orchestration execution paths based on the business feature words, the node load data, and the underlying data storage format identifier by executing a resource scheduling algorithm.

[0104] The instruction distribution and result output module is used to generate data processing instructions based on the service orchestration execution path, distribute the data processing instructions to the heterogeneous educational data storage nodes, collect the educational resource processing result data fed back by the heterogeneous educational data storage nodes, and output the educational resource processing result data.

[0105] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0106] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for intelligent service orchestration in a cross-domain heterogeneous education system, characterized in that, include: The system receives cross-domain educational resource acquisition request data packets through a network communication interface, performs parsing operations on the cross-domain educational resource acquisition request data packets, and extracts business feature words and global routing registry identifiers. Based on the global routing registry identifier, a state probe instruction is generated and concurrently sent to multiple heterogeneous educational data storage nodes in the distributed network to obtain the node load data and underlying data storage format identifier returned by the heterogeneous educational data storage nodes. Based on the business feature words, the node load data, and the underlying data storage format identifier, a resource scheduling algorithm is executed to construct a service orchestration execution path; Data processing instructions are generated based on the service orchestration execution path, and the data processing instructions are distributed to the heterogeneous educational data storage nodes. The educational resource processing result data fed back by the heterogeneous educational data storage nodes is collected and output.

2. The intelligent service orchestration method for a cross-domain heterogeneous education system according to claim 1, characterized in that, The step of parsing the cross-domain educational resource acquisition request data packet and extracting business feature words and global route registry identifiers includes: According to a preset network transmission protocol, the data packet for obtaining cross-domain educational resources is unpacked and extracted to generate the header feature field of the data packet and the payload content of the data packet. Based on a preset business feature dictionary, a string hash matching extraction operation is performed on the data packet payload content to generate business feature words; Extract the routing network identifier field from the feature fields of the data packet header, and use the routing network identifier field as the global routing registry identifier.

3. The intelligent service orchestration method for a cross-domain heterogeneous education system according to claim 2, characterized in that, The step of performing string hash matching extraction on the data packet payload content based on a preset business feature dictionary to generate business feature words includes: Convert the data packet payload content into a standard unified character encoding format text; Perform a sliding window hash value calculation operation on the standard unified character encoding format text to generate a payload text hash sequence; The payload text hash sequence is compared with the pre-stored hash nodes in the preset business feature dictionary tree, and the corresponding words that match successfully are output as business feature words.

4. The intelligent service orchestration method for a cross-domain heterogeneous education system according to claim 1, characterized in that, The step of generating state probe commands based on the global routing registry identifier and concurrently sending the state probe commands to multiple heterogeneous educational data storage nodes in the distributed network, and obtaining the node load data and underlying data storage format identifier returned by the heterogeneous educational data storage nodes, includes: The global route registry identifier is input into the preset global route service center to perform a matching query operation, generating a set of node network layer addresses; Extract multiple network layer protocol addresses from the node network layer address set, and establish underlying socket communication connections with multiple heterogeneous educational data storage nodes based on the multiple network layer protocol addresses respectively; Generate instruction data containing a heartbeat detection message, and use the instruction data as a status detection instruction; The status detection command is sent concurrently through the underlying socket communication connection; Receive response messages from multiple heterogeneous educational data storage nodes through the underlying socket communication connection, extract processor utilization and memory consumption values ​​from the response messages, and combine the processor utilization and memory consumption values ​​into the node load data; Extract the underlying data storage format identifier from the data dictionary description field of the response message.

5. The intelligent service orchestration method for a cross-domain heterogeneous education system according to claim 4, characterized in that, The method further includes: Based on the underlying data storage format identifier, a matching protocol conversion mapping table is queried in the preset local protocol conversion configuration library; Based on the protocol conversion mapping table, a format conversion matching algorithm is executed to generate data format reading rules.

6. The intelligent service orchestration method for a cross-domain heterogeneous education system according to claim 1, characterized in that, The step of constructing a service orchestration execution path by executing a resource scheduling algorithm based on the business feature words, the node load data, and the underlying data storage format identifier includes: Based on the business feature words, a computing power demand assessment algorithm is executed to generate a target computing resource consumption threshold. The node load data of multiple heterogeneous educational data storage nodes are compared with the target computing resource consumption threshold to select nodes that meet the computing power requirements as candidate educational data storage nodes. Extract the underlying data storage format identifier of the candidate educational data storage node, and calculate the data exchange latency cost between the candidate educational data storage nodes; Based on the principle of reducing the cost of data exchange latency, the service orchestration execution path is generated, which includes instructions on node interaction order and resource mapping relationship.

7. The intelligent service orchestration method for a cross-domain heterogeneous education system according to claim 6, characterized in that, The step of extracting the underlying data storage format identifier of the candidate educational data storage nodes and calculating the data exchange latency cost between the candidate educational data storage nodes includes: Compare the underlying data storage format identifiers of any two candidate educational data storage nodes to generate a format conversion requirement status value; When the format conversion requirement status value meets the preset triggering condition, the corresponding format conversion delay time value is extracted based on the pre-stored format conversion time consumption matrix. Obtain the network communication delay time value between the candidate educational data storage nodes, and perform a weighted summation of the network communication delay time value and the format conversion delay time value to generate a data exchange delay cost value.

8. The intelligent service orchestration method for a cross-domain heterogeneous education system according to claim 6, characterized in that, The process of generating data processing instructions based on the service orchestration execution path, distributing the data processing instructions to the heterogeneous educational data storage nodes, collecting educational resource processing result data fed back by the heterogeneous educational data storage nodes, and outputting the educational resource processing result data includes: Extract the resource mapping relationship instructions within the service orchestration execution path, and generate data processing instructions by combining them with a preset database connection pool protocol; The data processing instructions are sent to the heterogeneous educational data storage node according to a preset execution sequence; Open a local asynchronous communication receiving port and receive the educational resource processing result data stream returned by the heterogeneous educational data storage node through the local asynchronous communication receiving port; The data stream of educational resource processing results is spliced ​​and integrated to generate educational resource processing result data, which is then output through a user interface.

9. The intelligent service orchestration method for a cross-domain heterogeneous education system according to claim 8, characterized in that, The step of extracting the resource mapping relationship instruction within the service orchestration execution path and generating data processing instructions in conjunction with a preset database connection pool protocol includes: The resource mapping relationship instruction is subjected to syntax conversion to generate a standard query text stream corresponding to the underlying data storage format identifier; Based on the preset database connection pool protocol, an identity authentication identifier and a data packet verification code are added to the standard query text stream, and data processing instructions are encapsulated and generated.

10. An intelligent service orchestration system for cross-domain heterogeneous education systems, applied to the intelligent service orchestration method for cross-domain heterogeneous education systems as described in any one of claims 1-9, characterized in that, The system includes: The request data packet parsing module is used to receive cross-domain educational resource acquisition request data packets through a network communication interface, perform parsing operations on the cross-domain educational resource acquisition request data packets, and extract business feature words and global routing registry identifiers. The node status detection module is used to generate status detection instructions based on the global routing registry identifier, and concurrently send the status detection instructions to multiple heterogeneous educational data storage nodes in the distributed network to obtain the node load data and underlying data storage format identifier returned by the heterogeneous educational data storage nodes. The orchestration execution path construction module is used to construct service orchestration execution paths based on the business feature words, the node load data, and the underlying data storage format identifier by executing a resource scheduling algorithm. The instruction distribution and result output module is used to generate data processing instructions based on the service orchestration execution path, distribute the data processing instructions to the heterogeneous educational data storage nodes, collect the educational resource processing result data fed back by the heterogeneous educational data storage nodes, and output the educational resource processing result data.

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